Connection and intelligent management system for damageable heating element

Abstract

The invention discloses a connection and intelligent management system for vulnerable heating elements. The system comprises two insulating plates; the two conductive plates are arranged on the two insulating plates; the vulnerable heating elements are arranged between the two insulating plates, and the two ends of each vulnerable heating element are electrically connected with the two conductive plates through disconnecting devices respectively; the data collecting and preprocessing module is used for collecting historical data of the vulnerable heating element in the normal operation state and the abnormal operation state, and cleaning and normalizing the historical data; the fault prediction and disconnection decision module is used for predicting the fault of the vulnerable heating element according to the processed historical data and controlling a disconnection device to disconnect a circuit branch of the vulnerable heating element; and the power optimization distribution module is used for adjusting the power of the circuit branch of the vulnerable heating element according to the processed historical data. Therefore, the method has the beneficial effects of improving the production stability, enhancing the operation safety, realizing intelligent fault management, optimizing power distribution and the like.

Description

Technical Field

[0001] The invention relates to the field of energy power heating, and in particular to a connection and intelligent management system for vulnerable heating elements. Background Art

[0002] In industrial production, in heating systems involving electric heating elements such as silicon carbon rods or silicon molybdenum rods, especially in systems with multiple heating elements, existing wiring methods or wiring equipment have significant defects. When a small number of silicon carbon rods or silicon molybdenum rods are used, when a single silicon carbon rod or silicon molybdenum rod is replaced, all silicon carbon rods or silicon molybdenum rods need to be powered off, which will cause temperature fluctuations ranging from 50°C to 100°C. If a broken or deformed carbon rod is difficult to remove, the temperature fluctuation of the silicon carbon rod or silicon molybdenum rod will be greater, reaching several hundred degrees Celsius, which will lead to production interruptions and have a significant impact on product quality. When a large number of silicon carbon rods or silicon molybdenum rods are used, in order to avoid losses caused by power outages, enterprises adopt live operations. Although operators are required to wear safety equipment such as insulating boots and insulating gloves, safety accidents still occur frequently. These problems seriously affect the production efficiency of enterprises and bring major safety hazards. Summary of the invention

[0003] The present invention aims to solve the technical problems in the above-mentioned technology at least to some extent.

[0004] To this end, the present invention discloses a connection and intelligent management system for vulnerable heating elements, comprising:

[0005] Two insulating sheets;

[0006] Two conductive plates, which are respectively arranged on the two insulating plates;

[0007] A plurality of vulnerable heating elements, which are respectively arranged between the two insulating plates, and the two ends of each of them are respectively electrically connected to the two conductive plates through a disconnecting device;

[0008] The intelligent management system comprises:

[0009] A data collection and preprocessing module, used to collect historical data of the vulnerable heating element under normal operating conditions and abnormal operating conditions, and to clean and normalize the historical data;

[0010] a fault prediction and disconnection decision module, used to predict the fault of the vulnerable heating element according to the processed historical data and control the disconnection device to disconnect the circuit branch of the vulnerable heating element;

[0011] A power optimization allocation module is used to adjust the power of the circuit branch of the vulnerable heating element according to the processed historical data.

[0012] The connection and intelligent management system for vulnerable heating elements disclosed in the present invention has the following beneficial effects:

[0013] (1) Improving production stability. The present invention avoids power outage of all components when replacing a single vulnerable heating element, reduces temperature fluctuations, ensures stable production process, and improves product quality consistency;

[0014] (2) Enhanced operational safety. The present invention allows the management of a single vulnerable heating element without power outages. There is no need for live operation, which reduces safety risks, avoids accidents caused by live operation, and provides a safer environment for operators.

[0015] (3) Intelligent fault management, which can predict faults in advance and disconnect the circuit branches of faulty components in time, reduce production interruptions, and improve system reliability;

[0016] (4) Optimizing power distribution can optimize the power of each vulnerable heating element, improve energy utilization efficiency, reduce energy consumption, and extend the service life of the element.

[0017] In addition, the connection and intelligent management system for vulnerable heating elements disclosed in the present invention may also have the following additional technical features:

[0018] As a possibility, the fault prediction and disconnection decision module includes:

[0019] A feature engineering unit, used for extracting feature data from the processed historical data, and classifying the working state of the vulnerable heating element into two types: normal and fault according to the feature data;

[0020] A first training unit, used for dividing the feature data into training set data, validation set data and test set data, and training a support vector machine model by using the training set data, the validation set data and the test set data;

[0021] A fault prediction and decision unit is used to collect real-time electrical parameters of the fragile heating element at a preset sampling frequency according to the trained first model, and obtain a prediction result based on the collected real-time electrical parameters of the fragile heating element. When the prediction result reaches a fault threshold, a disconnecting device disconnects the circuit branch of the fragile heating element.

[0022] As a possibility, the power optimization allocation module includes:

[0023] A system modeling unit, for establishing a time state vector for a structure including a plurality of said vulnerable heating elements;

[0024] The second training unit is used to deploy the deep neural network architecture;

[0025] The loss function unit is used to set the temperature consistency loss function, power stability loss function and total loss function;

[0026] A power allocation unit is used to train the deep neural network architecture in the second training unit according to the historical data to minimize the total loss function in the loss function unit, input the time state vector based on the deep neural network architecture in the second training unit at this time, obtain the power adjustment amount of each of the fragile heating elements, and calculate the power allocation of each of the fragile heating elements at the next moment.

[0027] As a possibility, it also includes:

[0028] A human-computer interaction interface is used to display the curves of the temperature, current, power and resistance of each of the vulnerable heating elements changing with time.

[0029] As a possibility, the conductive plate includes, but is not limited to: an aluminum plate, a copper plate or a silver plate.

[0030] As a possibility, the disconnecting device includes, but is not limited to: a relay or an electric control switch.

[0031] As a possibility, it also includes:

[0032] A communication module is electrically connected to the data collection and preprocessing module, the fault prediction and disconnection decision module and the power optimization allocation module respectively.

[0033] As a possibility, the communication module adopts ModBus protocol.

[0034] Additional contents and advantages of the present invention will be given in the following description or may be understood through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The technical solutions and beneficial effects of the present invention will become apparent and easily understood from the following contents in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 It is a schematic diagram of the structure of the connection of a vulnerable heating element in one embodiment of the present invention;

[0037] Figure 2 is a system block diagram of an intelligent management system in one embodiment of the present invention;

[0038] Figure 3 A system block diagram of a fault prediction and disconnection decision module in one embodiment of the present invention;

[0039] Figure 4A system block diagram of a power optimization allocation module in one embodiment of the present invention;

[0040] As shown in the figure:

[0041] 1- insulating sheet, 2- conductive sheet, 3- vulnerable heating element, 4- disconnect device, 5- power cord. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] The connection and intelligent management system for vulnerable heating elements disclosed in the present invention will be described below with reference to the accompanying drawings.

[0044] like Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, a connection and intelligent management system for vulnerable heating elements includes:

[0045] Two insulating plates 1;

[0046] Two conductive plates 2, which are respectively arranged on two insulating plates 1;

[0047] A plurality of vulnerable heating elements 3, which are respectively arranged between two insulating plates 1, and two ends of each of them are respectively electrically connected to the two conductive plates 2 through a disconnecting device 4;

[0048] The power line 5 is connected to the two conductive plates 2 to form a loop;

[0049] The intelligent management system 100 comprises:

[0050] The data collection and preprocessing module 101 is used to collect historical data of the vulnerable heating element 3 under normal operation and abnormal operation, and clean the historical data to remove abnormal values, noise, etc., and perform normalization processing to make the historical data within a specific range, such as between 0 and 1, specifically, the temperature T, current I and resistance P of the vulnerable heating element 3 change with time t, and the sampling frequency is f;

[0051] A fault prediction and disconnection decision module 102, for predicting the fault of the vulnerable heating element 3 according to the processed historical data and controlling the disconnection device 4 to disconnect the circuit branch of the vulnerable heating element 3;

[0052] The power optimization allocation module 103 is used to adjust the power of the circuit branch of the vulnerable heating element 3 according to the processed historical data.

[0053] The fault prediction and disconnection decision module 102 includes:

[0054] The feature engineering unit 1021 is used to extract feature data from the processed historical data, and classify the working state of the vulnerable heating element 3 into two types: normal and fault according to the feature data;

[0055] For example, the rate of temperature change Among them, T t is the temperature of the vulnerable heating element 3 at a certain moment, T t+1 is the temperature of the vulnerable heating element 3 at the next moment, which reflects the speed of the temperature rise or fall of the vulnerable heating element 3, and the unit is ℃ / s;

[0056] Current fluctuation coefficient Where n is the number of sampling points in a specific time window, is the average value of the current in the time window, and this coefficient is used to measure the stability of the current passing through the vulnerable heating element 3;

[0057] Resistance power coefficient R(T)=R0(1+αP), where R0 is the initial resistance value of the vulnerable heating element 3 in the absence of power, P is the given power, and α is the power coefficient;

[0058] A first training unit 1022, used to divide the feature data into training set data, validation set data and test set data, and train a support vector machine model using the training set data, validation set data and test set data;

[0059] The relevant staff may select a support vector machine as a fault prediction model. The support vector machine is a binary classification model suitable for classifying the working state of the vulnerable heating element 3 into two categories: normal and faulty according to the extracted features;

[0060] Related work can define the hinge loss function, for a sample point (x i ,y i ), x i is the feature vector collected by feature engineering unit 1021, y i ∈{-1,1} represents the normal or fault category, and the loss function L(y i ,f(x i ))=max(0,1-y i f(x i )), where f(x i ) is the decision function of the support vector machine, which is in the form of f(x i )=ωx i+b, ω is the weight vector, b is the bias term, and the optimal ω and b are solved by minimizing the hinge loss function and using optimization methods such as the gradient descent algorithm;

[0061] The fault prediction and decision unit 1023 is used to collect the real-time electrical parameters of the vulnerable heating element 3 according to the preset sampling frequency based on the trained first model, and obtain the prediction result according to the collected real-time electrical parameters of the vulnerable heating element 3. When the prediction result reaches the fault threshold, the disconnection device 4 disconnects the circuit branch of the vulnerable heating element 3;

[0062] During the operation of the equipment, the temperature T, current I and resistance R of the vulnerable heating element 3 are collected in real time and form a characteristic vector x new , x new Input the trained support vector machine model and get the prediction result y pred If y pred = -1, indicating that the model predicts that the vulnerable heating element 3 is in a normal state. pred =1, indicating that the model predicts that the vulnerable heating element 3 may fail.

[0063] When a fault is predicted, the control system immediately issues a command to disconnect the corresponding vulnerable heating element 3 from the circuit by means of a disconnection device to prevent the fault from further deteriorating.

[0064] The power optimization allocation module 103 includes:

[0065] The system modeling unit 1031 is used to establish a time state vector for a structure including a plurality of vulnerable heating elements 3;

[0066] For a heating system with m silicon carbon rods, the state of the system at each time t is represented as a time state vector in, is the temperature of the i-th silicon carbon rod at time t, is its corresponding power;

[0067] A second training unit 1032, for deploying a deep neural network architecture;

[0068] The deep neural network architecture can use the long short-term memory network as the basic architecture. The long short-term memory network processes the time state vector S t Finally, a fully connected layer is used to output the power adjustment △P of each vulnerable heating element 3 i,t+1 , then the power of the i-th vulnerable heating element 3 at the next moment is P i,t+1 =P i,t +△P i,t+1 ;

[0069] A loss function unit 1033, used to set a temperature consistency loss function, a power stability loss function and a total loss function;

[0070] In order to achieve optimal power allocation, a loss function is designed to measure the difference between the system state and the ideal state;

[0071] Temperature consistency loss function in, is the average temperature of all vulnerable heating elements 3 at time t. This loss function causes the temperatures of the vulnerable heating elements 3 to be consistent;

[0072] Power stability loss function It limits the amplitude of power adjustment to avoid the impact of drastic power fluctuations on the system;

[0073] Total loss function: L total =αL T +βL P , where α and β are weight coefficients, and the ratio of the two is adjusted according to actual needs to balance the temperature consistency and power stability of the vulnerable heating element 3;

[0074] A power allocation unit is used to train the deep neural network architecture in the second training unit 1032 according to the historical data to minimize the total loss function in the loss function unit 1033, and input the time state vector based on the deep neural network architecture in the second training unit 1032 at this time, obtain the power adjustment amount of each fragile heating element 3, and calculate the power allocation of each fragile heating element 3 at the next moment;

[0075] In the actual operation of the equipment, the system time state vector S is collected in real time t , input the trained long short-term memory network model, and obtain the power adjustment value △P of each vulnerable heating element 3 i,t+1 , and then calculate the power allocation P at the next moment i,t+1 The control system adjusts the power supply to each vulnerable heating element 3 according to the calculation results to achieve automatic optimization of power distribution.

[0076] In addition, the connection and intelligent management system for vulnerable heating elements also includes:

[0077] A human-machine interactive interface, which is used to display the curves of the temperature T, current I, power P and resistance R of each vulnerable heating element 3 changing with time t;

[0078] In addition, the human-computer interaction interface can also display fault warning information, power distribution status or operation buttons and other interface contents;

[0079] For example, when the machine learning model predicts that the vulnerable heating element 3 may fail, the fault warning is highlighted on the interface, including the number of the vulnerable heating element 3 that may fail, the predicted fault type (such as "overtemperature risk", "unstable current", etc.) that may be obtained based on feature analysis, and the estimated time of the failure;

[0080] The conductive plate 2 includes, but is not limited to, an aluminum plate, a copper plate or a silver plate.

[0081] The disconnect device 4 includes, but is not limited to, a relay or an electric control switch.

[0082] The connection and intelligent management system for vulnerable heating elements also includes:

[0083] The communication module 104 is electrically connected to the data collection and preprocessing module 101 , the fault prediction and disconnection decision module 102 and the power optimization allocation module 103 respectively.

[0084] The communication module 104 adopts the ModBus protocol and realizes stable and efficient data transmission between modules through the standard ModBus communication interface and cables. For example, the data collection and preprocessing module 101 transmits the processed historical data to the fault prediction and disconnection decision module 102 and the power optimization allocation module 103 through the communication module 104, and the fault prediction and disconnection decision module 102 feeds back the prediction results and control instructions to the disconnection device 4 through the communication module 104;

[0085] In addition, the communication module 104 is responsible for coordinating the data transmission between the modules to ensure the accuracy and timeliness of the data. During the operation of the system, a large amount of data (such as real-time electrical parameters, prediction results, power adjustment amounts, etc.) is exchanged between the modules. The communication module 104 can effectively manage the flow of these data to avoid data conflicts and losses. For example, when the power optimization allocation module 103 calculates a new power allocation plan, the communication module 104 quickly transmits the relevant data to the control system, and the control system promptly adjusts the power supply to the vulnerable heating element 3 to ensure the stable operation of the system. At the same time, the communication module 104 can also communicate with external devices (such as a host computer monitoring system) to transmit the system's operating status and key data to external devices to achieve remote monitoring and management.

[0086] To summarize, the connection and intelligent management system for vulnerable heating elements disclosed in the present invention has the beneficial effects of improving production stability on the one hand. The present invention avoids power outages on all elements when replacing a single vulnerable heating element, reduces temperature fluctuations, ensures the stability of the production process, and improves product quality consistency. On the other hand, it has the beneficial effects of enhancing operational safety. The present invention allows management of a single vulnerable heating element without power outages, and no live operation is required, which reduces safety risks, avoids accidents caused by live operation, and provides operators with a safer environment. In addition, it also has intelligent fault management, which can realize early prediction of faults and timely disconnection of faulty component circuit branches, reduce production interruptions, and improve system reliability. Finally, it has the beneficial effects of optimizing power distribution, optimizing the power of each vulnerable heating element, improving energy utilization efficiency, reducing energy consumption, and extending the service life of components.

[0087] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A connection and intelligent management system for vulnerable heating elements, characterized in that: include: Two insulating sheets; Two conductive plates, which are respectively arranged on the two insulating plates; A plurality of vulnerable heating elements, which are respectively arranged between the two insulating plates, and the two ends of each of them are respectively electrically connected to the two conductive plates through a disconnecting device; The intelligent management system comprises: A data collection and preprocessing module, used to collect historical data of the vulnerable heating element under normal operating conditions and abnormal operating conditions, and to clean and normalize the historical data; A fault prediction and disconnection decision module, used to predict the fault of the vulnerable heating element according to the processed historical data and control the disconnection device to disconnect the circuit branch of the vulnerable heating element; A power optimization allocation module is used to adjust the power of the circuit branch of the vulnerable heating element according to the processed historical data.

2. A connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: The fault prediction and disconnection decision module comprises: A feature engineering unit, used for extracting feature data from the processed historical data, and classifying the working state of the vulnerable heating element into two types: normal and fault according to the feature data; A first training unit, used for dividing the feature data into training set data, validation set data and test set data, and training a support vector machine model by using the training set data, the validation set data and the test set data; A fault prediction and decision unit is used to collect real-time electrical parameters of the fragile heating element at a preset sampling frequency according to the trained first model, and obtain a prediction result based on the collected real-time electrical parameters of the fragile heating element. When the prediction result reaches a fault threshold, a disconnecting device disconnects the circuit branch of the fragile heating element.

3. The connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: The power optimization allocation module comprises: A system modeling unit, for establishing a time state vector for a structure including a plurality of said vulnerable heating elements; The second training unit is used to deploy the deep neural network architecture; The loss function unit is used to set the temperature consistency loss function, power stability loss function and total loss function; A power allocation unit is used to train the deep neural network architecture in the second training unit according to the historical data to minimize the total loss function in the loss function unit, input the time state vector based on the deep neural network architecture in the second training unit at this time, obtain the power adjustment amount of each of the fragile heating elements, and calculate the power allocation of each of the fragile heating elements at the next moment.

4. The connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: Also includes: A human-computer interaction interface is used to display the curves of the temperature, current, power and resistance of each of the vulnerable heating elements changing with time.

5. The connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: The conductive plate includes, but is not limited to, an aluminum plate, a copper plate or a silver plate.

6. The connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: The disconnecting device includes, but is not limited to, a relay or an electric control switch.

7. The connection and intelligent management system for vulnerable heating elements according to claim 1, characterized in that: Also includes: A communication module is electrically connected to the data collection and preprocessing module, the fault prediction and disconnection decision module and the power optimization allocation module respectively.

8. A connection and intelligent management system for vulnerable heating elements as claimed in claim 7, characterized in that: The communication module adopts ModBus protocol.

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